基于山区地质灾害危险性分区的建设用地适宜性评价

地质灾害是建设用地选址时考虑的关键因素之一,也是危险性区划的主要参考要素,其直接或间接危害到人类安全并给社会和经济建设造成重大损失。为了降低建设用地规划中地质灾害的影响,应将地质灾害危险性分区考虑到建设用地适宜性评价当中。以干旱河谷山区丹巴县为研究区,运用确定系数和逻辑回归相结合的方法,构建地质灾害危险性评价体系对该区进行地质灾害危险性分区。选择安全因子、自然因子、社会因子和生态因子共同构成建设用地适宜性评价指标体系,利用层次分析法对研究区进行建设用地适宜性评价。结果表明:(1)对丹巴县进行危险性分区,极高危险区217.20km2,高危险区744.95km2,中度危险区1352.44km2,低度危险区1295.08km2以及极低危险区875.32km2。(2)丹巴县建设用地最适宜区面积为115.72km2,占县域总面积的2.57%,适宜建设的土地少且多集中在小金川、革什扎河、东谷河和大渡河沿岸河谷地区。较适宜区、基本适宜区、较不适宜区和禁止建设区面积分别为683.47km2、623.21km2、590.66km2、2494.21km2。(3)已建成的城镇空间中38.50%和37.62%分布在最适宜区和较适宜区,整体分布状况良好。有5.85%的城镇空间建设在较不适宜区,2.67%在禁止建设区,存在生态安全风险,该区域应加强灾害预警与防灾减灾工作,有条件地适度搬迁。     

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

Neural Computing and Applications - In this paper, an image cipher is presented based on DNA sequence operations, image filtering and memrisitve chaotic system. Firstly, plain image is preprocessed...     

Exploiting 2D compressed sensing and information entropy for secure color image compression and encryption

Neural Computing and Applications - Compared to 1D compressed sensing (CS), 2D CS is more efficient for compressing the plaintext image from two directions, but security level of current 2D...     

面向IC装备功能仿真平台的多模式测试方法

为了实现全面对IC装备以及装备功能仿真平台进行功能测试,通过分析装备命令响应特点,提出了包含单指令测试、逻辑指令序列测试以及随机指令序列测试的多模式测试方法;单指令测试验证设备以及由相关设备组成的系统的单指令响应正确性;逻辑指令序列从控制系统角度验证设备或系统的连续动态运行性能;随机指令序列测试验证设备或系统的动态随机响应性能;实际应用表明,提出的测试方法可以满足IC装备功能仿真平台的测试需求。     

基于LabVIEW的惯性测量单元信号采集及处理系统设计

利用LabVIEW设计了惯性测量单元信号采集及处理软件,主要包括串口通信、数据分离及信号处理等模块;串口通信模块采用VISA方式编程,建立了惯性测量单元与上位机之间的通信;根据惯性测量单元输出数据的格式,利用LabVIEW中的匹配模式函数分离出沿三轴向的加速度信号和角速率信号;设计了一种用于去除加速度信号中低频成份的数字滤波器;在建立的陀螺漂移模型基础上,分别进行小波基、小波分解层数、阈值函数及阈值估计方法等小波参数的选取.     

CLARO: modeling and processing uncertain data streams

Uncertain data streams, where data are incomplete and imprecise, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the claro system that supports stream processing for uncertain data naturally captured using continuous random variables. claro employs a unique data model that is flexible and allows efficient computation. Built on this model, we develop evaluation techniques for relational operators by exploring statistical theory and approximation. We also consider query planning for complex queries given an accuracy requirement. Evaluation results show that our techniques can achieve high performance while satisfying accuracy requirements and outperform state-of-the-art sampling methods.     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

First order sensitivity analysis of flexible multibody systems using absolute nodal coordinate formulation

Design sensitivity analysis of flexible multibody systems is important in optimizing the performance of mechanical systems. The choice of coordinates to describe the motion of multibody systems has a great influence on the efficiency and accuracy of both the dynamic and sensitivity analysis. In the flexible multibody system dynamics, both the floating frame of reference formulation (FFRF) and absolute nodal coordinate formulation (ANCF) are frequently utilized to describe flexibility, however, only the former has been used in design sensitivity analysis. In this article, ANCF, which has been recently developed and focuses on modeling of beams and plates in large deformation problems, is extended into design sensitivity analysis of flexible multibody systems. The Motion equations of a constrained flexible multibody system are expressed as a set of index-3 differential algebraic equations (DAEs), in which the element elastic forces are defined using nonlinear strain-displacement relations. Both the direct differentiation method and adjoint variable method are performed to do sensitivity analysis and the related dynamic and sensitivity equations are integrated with HHT-I3 algorithm. In this paper, a new method to deduce system sensitivity equations is proposed. With this approach, the system sensitivity equations are constructed by assembling the element sensitivity equations with the help of invariant matrices, which results in the advantage that the complex symbolic differentiation of the dynamic equations is avoided when the flexible multibody system model is changed. Besides that, the dynamic and sensitivity equations formed with the proposed method can be efficiently integrated using HHT-I3 method, which makes the efficiency of the direct differentiation method comparable to that of the adjoint variable method when the number of design variables is not extremely large. All these improvements greatly enhance the application value of the direct differentiation method in the engineering optimization of the ANCF-based flexible multibody systems.     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.     

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为载体的铁离子(Ⅲ)电极的研究

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为中性载体制备了PVC膜Fe3+离子选择性电极。该电极对Fe3+呈现出良好的选择性和近Nernst电位响应性能。电极斜率为21mV/dec,线性范围为3.0×10-5～1.0×10-1mol/L,检出限为1.0×10-5mol/L。采用交流阻抗技术研究了电极的响应机理,并将电极作为指示电极初步用于EDTA的电位滴定。